
@inproceedings{kenterSiameseCBOWOptimizing2016,
  address = {Berlin, Germany},
  title = {Siamese {{CBOW}}: {{Optimizing Word Embeddings}} for {{Sentence Representations}}},
  shorttitle = {Siamese {{CBOW}}},
  booktitle = {Proceedings of the 54th {{Annual Meeting}} of the {{Association}} for {{Computational Linguistics}} ({{Volume}} 1: {{Long Papers}})},
  publisher = {{Association for Computational Linguistics}},
  author = {Kenter, Tom and Borisov, Alexey and {de Rijke}, Maarten},
  month = aug,
  year = {2016},
  pages = {941--951},
  file = {C:\\Users\\Chen\\Zotero\\storage\\VRH2VDIJ\\Kenter 等。 - 2016 - Siamese CBOW Optimizing Word Embeddings for Sente.pdf}
}

@article{nagoudiLIMLIGSemEval2017Task12017,
  title = {{{LIM}}-{{LIG}} at {{SemEval}}-2017 {{Task1}}: {{Enhancing}} the {{Semantic Similarity}} for {{Arabic Sentences}} with {{Vectors Weighting}}},
  author = {Nagoudi, El Moatez Billah and Ferrero, J{\'e}r{\'e}my and Schwab, Didier},
  month = aug,
  year = {2017}
}

@article{mikolovEfficientEstimationWord2013,
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1301.3781},
  primaryClass = {cs},
  title = {Efficient {{Estimation}} of {{Word Representations}} in {{Vector Space}}},
  abstract = {We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.},
  journal = {arXiv:1301.3781 [cs]},
  author = {Mikolov, Tomas and Chen, Kai and Corrado, Greg and Dean, Jeffrey},
  month = jan,
  year = {2013},
  keywords = {Computer Science - Computation and Language},
  file = {C:\\Users\\Chen\\Zotero\\storage\\AH6QAWQL\\Mikolov 等。 - 2013 - Efficient Estimation of Word Representations in Ve.pdf;C:\\Users\\Chen\\Zotero\\storage\\5AEZKEHC\\1301.html}
}

@article{robertsonProbabilisticRelevanceFramework2009,
  title = {The Probabilistic Relevance Framework: {{BM25}} and Beyond},
  volume = {3},
  shorttitle = {The Probabilistic Relevance Framework},
  number = {4},
  journal = {Foundations and Trends\textregistered{} in Information Retrieval},
  author = {Robertson, Stephen and Zaragoza, Hugo},
  year = {2009},
  pages = {333--389},
  file = {C:\\Users\\Chen\\Zotero\\storage\\DCPSQCMD\\Robertson 和 Zaragoza - 2009 - The probabilistic relevance framework BM25 and be.pdf;C:\\Users\\Chen\\Zotero\\storage\\A8ZAW7ZE\\INR-019.html}
}


